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  Dealing with large Diagonals in Kernel Matrices

Weston, J., Schölkopf, B., Eskin, E., leslie, C., & Noble, W. (2003). Dealing with large Diagonals in Kernel Matrices. Annals of the Institute of Statistical Mathematics, 55(2), 391-408. doi:10.1007/BF02530507.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-DC41-0 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-6A8A-9
Genre: Journal Article

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 Creators:
Weston, J1, 2, Author              
Schölkopf, B1, 2, Author              
Eskin, E, Author
leslie, C, Author
Noble, WS, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well: We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine, which is a common kernel approach for pattern recognition.

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 Dates: 2003-06
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/BF02530507
BibTex Citekey: 1866
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Title: Annals of the Institute of Statistical Mathematics
Source Genre: Journal
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Pages: - Volume / Issue: 55 (2) Sequence Number: - Start / End Page: 391 - 408 Identifier: ISSN: 0563-6841
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000267710